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Objective In stereotactic neurosurgeries，the thalamus and its substructure nerve nuclei have been regarded as the target areas, which can be usually used to treat epilepsy and extrapyramidal diseases. The utilization of computers for the segmentation of thalamus nerve nuclei has significant value of research in the diagnosis and treatment of neurosurgical diseases. In this paper, an algorithm based on adaptive fuzzy connectedness combined with confidence connectedness is proposed to improve the accuracy of thalamus segmentation result, simplify manual operation, reduce human intervention and avoid subjective influence. Methods This method gains the image gradient feature in the original framework based on the theory of fuzzy connectedness. Adaptive weighting and the way of automatically selecting interested area can reduce manual intervention. The thalamus structures of 10 cases of human brain MRI image data are segmented. Results The experimental results are compared with the results of the manual segmentation guided by experts. The similarity degree of them is calculated for quantitative comparison. The accuracy of this algorithm is higher, with human intervention reduced. Conclusions The adaptive fuzzy connectedness combined with confidence connectedness algorithm is better than traditional theory of fuzzy connectedness in computation speed and accuracy.